1. Field of the Invention
This invention relates to the field of risk management. More specifically, the invention comprises a method and a system for extracting hazard information from forecast data having varied temporal and spatial accuracies.
2. Description of the Related Art
Hazards and the use of hazard predictions are a significant concern for many industries, especially the aviation industry. Accordingly, the present invention is described and considered as it applies to aviation. The description of the related art will also refer generally to the aviation application. However, in reading this entire disclosure, the reader should bear in mind that the methods disclosed can be applied to many areas beyond aviation.
The general approach to hazard prediction in the aviation industry has been to utilize forecast data and other weather products which are commonly shared among various “users,” such as dispatchers, pilots, and controllers. Each of the users works to ensure that the aircraft avoids flying in unacceptably hazardous weather.
The weather information is generated by weather forecasters in various formats (textual, graphical, or as gridded values or probabilities in large increments of time). The weather may be “observations” of weather as it was at a particular time, which by the time of receipt is actually in the past. Alternatively, weather information may be supplied as “forecasts.” Forecasts are normally generated for periods of time into the future, again set in large increments of time (from several hours to several days). Forecasts generally describe the expected weather conditions rather than actual hazards.
These weather products in their current form require human interpretation. Furthermore, meaningful and accurate interpretation requires significant skill and experience. The aircraft operators are primarily interested in weather that will be dangerous to their aircraft operations and in weather conditions—such as winds and temperatures—that affect the efficiency of their flights. The users of the weather products therefore attempt to interpret meteorological data to find where hazards and favorable conditions exist. In addition, users typically need to access several different weather products and mentally integrate the information from them in order to develop a complete picture.
One common weather product is referred to as a Collaborative Convective-weather Forecast Product (“CCFP”). These forecasts often contain highly subjective values such as “confidence.” Such qualitative values are difficult to use as inputs for other tools. Such forecasts are often presented in large time increments, often in hours. The reason for the large time increment is the amount of automated and manual data processing that is required for the generation of the forecasts. The user receives many weather products, and these products are often not in agreement and are not for the actual time in which the user is interested. The user of these products therefore needs to have some meteorological knowledge to judge which of the products to believe, to interpolate between the times of effectiveness of the products, and then to generate an assessment of the level of probability of hazards implied by the weather forecast.
The further into the future the prediction is carried, the less certainty there is that the forecasts will be correct. This is especially true of convective weather forecasting. Convective weather is the source of turbulence, hail and lightning, all of which are hazards to aviation. The certainty of the forecast is normally expressed as a “probability” of the forecast weather occurring. With the convective weather forecasting example, this is stated in terms of “radar cloud tops,” and “likely percentage coverage of a several thousand square mile area” reader will note that the CCFP does not express probabilities of the hazards such as turbulence in objectively quantifiable terms specific to turbulence. Even when turbulence is forecast by some products it is in subjective values such as “moderate.” Of course, turbulence that is moderate for a large aircraft may be severe for a small one.
Users who are planning flights are required to identify hazards to the flight and attempt to quantify them and their affect on their aircraft. However, the user is presented with conflicting views of weather from the various data sources. The large time between forecast updates is also a problem, since a first available forecast may be for a point in time one hour before the flight passes a point and the next forecast an hour after the flight has passed.
FIGS. 1 and 2 illustrate the problem of using historical weather data. FIG. 1 shows weather data for the continental United States at the flight planning stage. Aircraft 16 is to fly from Los Angeles, Calif. (denoted as origin 12) to Atlanta, Ga. (denoted as destination 14) along planned route 10. The dispatcher typically evaluates the route approximately 1 hour before takeoff. The weather data may be 30 minutes old when the dispatcher evaluates the route. The weather data of FIG. 1 illustrates a moving storm front 18 with associated storm cells. Storm front 18 intersects a portion of planned route 10 at the time the weather was observed.
As shown in FIG. 2, by the time aircraft 16 is within 2 hours of destination 14, storm front 18 has moved beyond destination 14. In this example, the dispatcher may have correctly predicted that planned route 10 would avoid storm front 18.
In the example illustrated in FIGS. 1 and 2, the dispatcher used radar data as proxies for hazardous weather conditions. Weather data is not always a reliable proxy for predicting a hazardous condition. Radar returns generally show raindrop density. As illustrated in FIG. 3, a radar return illustrates the presence of storm cell 20 and storm cell 22. Regions 30 denote areas of heaviest rain. Regions 28, 26, and 24 illustrate heavy rain, moderate rain, and light rain respectively. An inexperienced dispatcher viewing weather data as proxies for hazardous conditions might look at such a radar return and determine that flying between storm cell 20 and storm cell 22 would be the safest route. Severe turbulence zone 34 actually exists between storm cell 20 and 22—an area the proxy data suggests should be free and clear of hazardous conditions. In addition, hail can be blown well clear of the hazard area indicated by the proxy as illustrated by potential hail zones 32.